Where \(\psi _{a_i}\)  (\(\psi _{v_j}\) ) are the embeddings of audio (video) input at time step i (j). In order to emphasize our method's advantage over the literature, we employ the same architecture for the audio and video encoders (\(E^a\)  and \(E^v\) ) as the one used in SyncNet [1]}. This architecture consists of 3D convolutions (operating also on the temporal domain) with max-pooling layers, ending with fully connected layers. Batch normalization layers are added after each convolutional layer.
